Healthcare systems require a paradigm shift in the way healthcare services are delivered to counteract demographic changes in patient populations, expanding technological developments and the increasing complexity of healthcare. Participatory design (PD) is a methodology that promotes the participation of users in the design process of potential telehealth applications. A PD project can be divided into four phases including: the identification and analysis of participant needs; the generation of ideas and development of prototypes; testing and further development of prototypes; and evaluation. PD is an iterative process where each phase is planned by reflecting on the results from the previous phase with respect to the participants’ contribution. Key activities of a PD project include: fieldwork; literature reviewing; and development and testing. All activities must be applied with a participatory mindset that will ensure genuine participation throughout the project. Challenges associated with the use of PD include: the time required to properly engage with participants; language and culture barriers amongst participants; the selection of participants to ensure good representation of the user group; and empowerment. PD is an important process, which is complemented by other evaluation strategies that assess organisational requirements, clinical safety, and clinical and cost effectiveness. PD is a methodology which encourages genuine involvement, where participants have an opportunity to identify practical problems and to design and test technology. The process engages participants in storytelling, future planning and design. PD is a multifaceted assessment tool that helps explore more accurately clinical requirements and patient perspectives in telehealth.

Participatory design methods in telemedicine research

BACKGROUND: Blended physiotherapy, in which physiotherapy sessions and an online application are integrated, might support patients in taking an active role in the management of their chronic condition and may reduce disease related costs. The aim of this study was to evaluate the cost-effectiveness of a blended physiotherapy intervention (e-Exercise) compared to usual physiotherapy in patients with osteoarthritis of hip and/or knee, from the societal as well as the healthcare perspective. METHODS: This economic evaluation was conducted alongside a 12-month cluster randomized controlled trial, in which 108 patients received e-Exercise, consisting of physiotherapy sessions and a web-application, and 99 patients received usual physiotherapy. Clinical outcome measures were quality-adjusted life years (QALYs) according to the EuroQol (EQ-5D-3 L), physical functioning (HOOS/KOOS) and physical activity (Actigraph Accelerometer). Costs were measured using self-reported questionnaires. Missing data were multiply imputed and bootstrapping was used to estimate statistical uncertainty. RESULTS: Intervention costs and medication costs were significantly lower in e-Exercise compared to usual physiotherapy. Total societal costs and total healthcare costs did not significantly differ between groups. No significant differences in effectiveness were found between groups. For physical functioning and physical activity, the maximum probability of e-Exercise being cost-effective compared to usual physiotherapy was moderate (

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Cost-effectiveness of a blended physiotherapy intervention compared to usual physiotherapy in patients with hip and/or knee osteoarthritis

An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s disease

Atlas of Sleep Medicine 

by Sudhansu Chokroverty, Robert J. Thomas, and Meeta Bhatt, 376 pp., Elsevier, 2005, $99.00

In contrast, the Atlas of Sleep Medicine is an atlas of polysmonographic studies. It goal is to introduce the basics of polysomnography.

The first part of the atlas reviews electroencephalography, scoring sleep stages and …

Atlas of Sleep Medicine

The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV) A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control We have developed a novel point-process-based method for real-time R-R interval error detection and correction Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model We test our algorithm over two datasets from the PhysioNet archive The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device

A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats

Background: Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD.
Objective: This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF).
Methods: In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD.
Results: The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P<.01).
Conclusions: A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.

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Papers

Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis